This disclosure concerns computed tomographic image reconstruction of dynamic objects. In particular, dynamic changes of the attenuation coefficients of the object (patient) are considered which typically occur when contrast agent is administered to the patient. In perfusion studies one is interested especially in such dynamic changes to compute perfusion properties of the tissue.
Nowadays perfusion computed tomography (CT) protocols typically acquire a complete set of projection data every second. Each set of projection data is interpreted as data of a static object and is processed by conventional reconstruction algorithms. The drawbacks of neglecting dynamic properties in the reconstruction step are manifold. The acquisition tie of a single set of projection data has to be short enough to justify the assumption of a static object during the acquisition. This restricts significantly the freedom in the sampling rate and enforces a sampling much denser than would be required by the frequency spectrum of the time attenuation curves (TAC) of the object.
Dynamic computed tomography (CT) has already found its way into clinical routine for visualization of functional processes. See K. Miles, P. Dawson, and M. Blomley, Functional Computed Tomography, Isis Medical Media, 1997; E. Klotz and M. König, “Perfusion Measurements of the Brain; Using Dynamic CT for the Quantitative Assessment of Cerebral Ischemia in Acute stroke,” European Journal of Radiology, vol. 30, pp. 170-184, 1999. For example, for stroke patients the damage in the brain can be estimated by assessing parameters like perfusion and blood volume, time to peak, etc. Perfusion CT is a very fast, stable and accurate method easily available due to the widespread distribution of CT scanners.
In a typical perfusion CT protocol, after injection of a contrast agent, projection data are acquired continuously for a period of time of up to 40 seconds. A temporal sequence of slice images of a region of interest (ROI) is reconstructed. Typically one time frame is computed per scanner rotation with a rotation time of 1 second. The temporal evolution of the contrast enhancement (time-attenuation curves) due to the flow of contrast agent through the vessels and tissue is used to compute the functional parameters.
During the acquisition of the set of projections necessary to reconstruct an image, dynamic changes are ignored and each time frame is computed as in the static case. The introduction of large area detectors will allow the simultaneous scanning of an entire region of interest, thus enabling perfusion studies of an entire volume. The accompanying increase of clinical relevance will enforce quality improvements by incorporating dynamic properties into the reconstruction process
Reconstruction from projection data of a dynamically changing object is a severe problem. Each projection is acquired at a different time representing the object in a different state. Thus the obtained data sets are inconsistent. If the dynamic changes in the object are fast relative to the rotational speed of the gantry, this leads to artifacts in the reconstructed frames around dynamically changing regions and errors in the reconstructed value of the attenuation coefficient within them.
Several approaches have been proposed in the literature to overcome the above-described problem. Taguchi suggested to use a generalized Parker-like weighting window to compensate the mismatch between projections at the endpoints of the scan. K. Taguchi, “Temporal resolution and the Evaluation of Candidate Algorithms for Four Dimensional CT,” Medical Physics, vol. 30, no. 4, pp. 640-650, April 2003. See also A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging. IEEE Press, 1988. A significant reduction of artifacts was observed. A more sophisticated method was proposed by Grangeat et al. where an estimate of data at any time instance was achieved by linear regression. P. Grangeat, A. Koenig, T. Rodet, and S. Bonnet, “Theoretical Framework for a Dynamic Cone-beam Reconstruction Algorithm based on a Dynamic Particle Model,” Physics in Medicine and Biology, vol. 47, no. 15, August 2002.